Pitch grades like Stuff+, Location+, and Pitching+ measure current skill on a per-pitch basis. But front offices and fantasy managers need to know: what will this pitcher actually do next season? Projections bridge that gap.
The Projection Pipeline
INPUT 1
Pitch-level grades
Stuff+, Location+, and Pitching+ for each pitch type tell us how good the pitcher's raw tools are right now.
INPUT 2
Historical performance
Prior-year K%, BB%, ERA, and 2-year averages. Skills are sticky year-to-year, so the best predictor of future K% is past K%.
INPUT 3
Context & aging
Age, role (starter vs. reliever), workload, and arsenal composition all shift the baseline expectation.
OUTPUT
K%, BB%, and kwERA
Three numbers that distill a pitcher's projected run-prevention ability based on the things most within their control.
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Why kwERA? ERA is noisy -- it depends on defense, sequencing luck, and park factors. kwERA (5.40 - 12*K% + 16*BB%) strips all that away and focuses on the two outcomes a pitcher controls most: strikeouts and walks. Lower is better -- an elite pitcher might project for a 3.00 kwERA, while a replacement-level arm sits around 5.00.
2. What Drives K% and BB%?
The model has 231 features, but most of the predictive power concentrates in a handful of categories. Use the toggle to switch between strikeout and walk drivers.
Top Predictors of Projected Strikeout Rate
Strikeouts are driven by a combination of historical track record (the strongest signal) and current pitch quality. A pitcher with elite Stuff+ on breaking balls and a deep arsenal that tunnels well will generate whiffs. But even the best raw stuff can't overcome a career-long inability to miss bats.
Top Predictors of Projected Walk Rate
Walk rate is the stickiest skill in baseball. A pitcher who walked 8% of batters last year will almost certainly walk close to 8% next year. Location+ (command quality) adds incremental signal -- pitchers who consistently hit their spots walk fewer batters. Count tendencies matter too: pitchers who get ahead 0-1 rarely walk the batter.
3. Why Linear, Not XGBoost?
Stuff+, Location+, and Pitching+ all use XGBoost. So why does the Projections model use a simple linear model? The answer comes down to sample size and feature count.
The Dimensionality Problem
XGBoost with 231 features & ~500 rows
Would overfit catastrophically
Trees can memorize individual pitcher-seasons
Can find "patterns" in 2 data points
No built-in way to ignore irrelevant features
Would look great on training data, terrible on new seasons
ElasticNet (L1 + L2 regularization)
Built for this exact scenario
L1 (Lasso): Drives weak feature weights to exactly zero
L2 (Ridge): Shrinks remaining weights to prevent overfit
Only ~30-50 features end up mattering
Stable predictions that hold up on future seasons
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Analogy: XGBoost is like giving a detective unlimited time and a massive corkboard -- great when there are millions of clues to cross-reference. ElasticNet is like telling the detective: "You have 500 case files and 231 possible leads. Most are dead ends. Find the 30 that actually matter and ignore the rest." When evidence is scarce, disciplined focus beats elaborate theories.
4. Aging Curves
Pitcher performance isn't static. The model accounts for where a pitcher sits on their career arc, adjusting projections based on age-related trends across the entire MLB population.
Aging Effects on K% and BB%
Hover over the curve to see values at each age.
K% AGING
Sharp peak, steady decline
Strikeout ability peaks at age 26-28 when velocity and stuff are at their best. The decline accelerates after 32 as velocity fades and pitch sharpness erodes.
BB% AGING
Remarkably flat
Command is a learned skill that doesn't require elite athleticism. Walk rates barely change from age 25-35, only rising modestly in late career. This is why BB% is so predictable.
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What this means for projections: A 27-year-old with a 24% K-rate gets a small downward age adjustment -- he's near peak. A 33-year-old with the same K-rate gets a larger downward adjustment because the model expects his K% to keep declining. Neither pitcher's BB% moves much from aging alone.
5. Interpreting Projections
A projection is a center of gravity, not a promise. It represents the most likely outcome given typical health and workload, but real seasons involve variance.
Distribution of Possible Outcomes
Important Caveats
CAVEAT 1
Assumes typical health
Projections don't predict injuries. A torn UCL or shoulder issue resets everything. The projection is what we'd expect if the pitcher stays healthy and throws a full workload.
CAVEAT 2
Role changes shift the baseline
A starter who moves to the bullpen typically gains 1-2 mph of velocity and 2-3 points of K%. Projections assume the pitcher stays in their current role unless specified otherwise.
CAVEAT 3
Not capturing hot streaks
If a pitcher had an incredible April, the projection doesn't spike up. Projections estimate true talent over a full season, smoothing out short-term variance.
CAVEAT 4
Uncertain for young pitchers
With limited MLB track record, projections for rookies and 2nd-year pitchers carry wider uncertainty bands. The model relies more on pitch grades and less on historical stats for these arms.
What Projections Are Good At
Ranking pitchers
Even if absolute values are off by 0.3 kwERA, relative rankings are stable. If we project Pitcher A as better than Pitcher B, that usually holds.
Identifying skill changes
When a pitcher's projection shifts significantly year-to-year, it flags a real underlying change -- new pitch, velocity loss, or mechanical adjustment.
Separating skill from luck
A 2.50 ERA pitcher who projects for a 4.00 kwERA probably benefited from great defense or strand rate luck. Projections cut through the noise.
Long-term accuracy
Over many pitchers and seasons, the projected K% and BB% closely track actual results. The model is calibrated -- it doesn't systematically over- or under-predict.